Given the building sector's significant impact on energy consumption and CO2 emissions, there is a clear need for a sustainable solution. Here, smart building technologies are emerging as a promising response, as they offer the potential to improve operational efficiency while reducing environmental impact. However, the development of smart building solutions for the building sector often follows a technology-driven approach, in which technical feasibility and overall system optimization are prioritized over occupants’ perspectives, preferences, and behavior. As a result, building systems and technologies are not fully aligned with how occupants use and interact with these spaces. Bridging the gap between theoretical knowledge and practical application is, therefore, crucial for unlocking the full potential of smart building technology.

This study focused on enhancing our understanding of the interaction between smart building technology and users. A specific focus is placed on climate control technology within the broader landscape of smart building systems. By employing an exploratory approach, in which a combination of qualitative and quantitative methods was utilized, smart building users’ preferences and behavioral mechanisms were explored. Through semi-structured interviews with smart building occupants, the study delved deeper into understanding the motivations, barriers, and other aspects of occupant behavior and interactions with smart building technology. Additionally, insights from interviews with industry experts provided valuable perspectives on which key factors contribute to the increased adoption of smart building technology. These qualitative findings were complemented by quantitative data collected through an online survey (n=92), which allowed trends and preferences to be identified from a larger group of smart building occupants.

The study showed that the successful implementation of smart building technology goes beyond mere technological details. It provided empirical insights into the preferences and behavioral mechanisms of smart building occupants, filling a gap in the existing literature. By exploring these aspects, the research deepened our understanding of user interactions within smart buildings. Consequently, it advocates for a user-centered approach to smart building initiatives, emphasizing the importance of understanding human preferences and incorporating user feedback to effectively adapt technology to their specific needs and routines. However, the study revealed a diverse range of user preferences and motivations regarding interaction with smart building technology, including preferences regarding control levels, system feedback, transparency, flexibility, and interface design. This diversity highlighted the absence of a one-size-fits-all approach to implementing smart building technology. Consequently, specific practical recommendations were developed to guide stakeholders in smart building initiatives, emphasizing a more user-centered implementation that ensures adaptability to the unique needs and preferences of building users.

The 21st century has seen a significant increase in the global prevalence of mental health problems, affecting almost a billion people. These conditions not only reduce the quality of life for individuals but also lead to stigmatization, discrimination, and social isolation. The COVID-19 pandemic has further exacerbated mental health issues, with depression and anxiety becoming the most common mental disorders among young workers. However, encouraging young adults to seek help for mental health is challenging.

To address this issue, the concept of digitalizing psychotherapies through mental health chatbot services is proposed as a potential solution. Giving the chatbot a voice to communicate is suggested to increase perceived trust and intention to use among users. A research question is posed: "How does vocal communication and its personalization in chatbots affect the intention to use of young adults with depression?"

The study conducted an online survey using Qualtrics with three groups of participants: the control group (text-based communication) and two treatment groups (vocal communication, and personalized vocal communication). The chatbot followed a rigid script based on a psychotherapy protocol called behavioral activation. Data analysis involved Mann-Whitney U tests, ordinal logistic regression, and one-way ANOVA for randomization check.

Findings from the study indicated that chatbots communicating through speech did not significantly influence trust compared to text-based chatbots. Personalization of the voice did not increase trust based on emotions and even negatively affected cognitive trust. Both cognitive and affective trust were positively associated with the intention to use chatbots. Cognitive trust indirectly mediated the relationship between personalized vocal communication and intention to use.

The study contributed to existing literature by exploring the adoption of conversational agents for mental health support, focusing on young adults with depression. It also provided insights into the use of speech in chatbots and the impact of personalization on trust and chatbot adoption. Additionally, the study shed light on the adoption of mental health chatbots in developing countries, which lack access to healthcare.

From a managerial and societal standpoint, the study's findings have implications for companies developing chatbots for mental healthcare. The insights gained can improve the design of mental health chatbots, potentially easing the burden on mental health professionals and contributing to long-term societal well-being. Moreover, the study highlights the potential benefits of chatbot accessibility for users in developing countries with limited access to healthcare.

Overall, the research provides valuable knowledge about the adoption of chatbots for mental health support, voice communication, personalization, and its implications for various demographics and regions. It also raises ethical considerations, particularly regarding gender-based biases when using gender-specific voices for chatbots.